3 papers
cs.LG2026
Batch Distillation Data for Developing Machine Learning Anomaly Detection Methods
Justus Arweiler, Indra Jungjohann, Aparna Muraleedharan +5
Machine learning (ML) holds great potential to advance anomaly detection (AD) in chemical processes. However, the development of ML-based methods is hindered by the lack of openly…
physics.chem-ph2026
Hybrid Machine Learning for Enhanced Prediction of Diffusion Coefficients in Liquids
Jens Wagner, Zeno Romero, Kerstin Münnemann +4
Diffusion coefficients are key thermophysical properties for modeling mass transport in liquids, but experimental data are scarce, making reliable prediction methods indispensable.…
cs.LG2026
Prediction of Diffusion Coefficients in Mixtures with Tensor Completion
Zeno Romero, Kerstin Münnemann, Hans Hasse +1
Predicting diffusion coefficients in mixtures is crucial for many applications, as experimental data remain scarce, and machine learning (ML) offers promising alternatives to estab…